Separation in Data Mining Based on Fractal Nature of Data

0393276 - ÚI 2014 RIV HK eng J - Článek v odborném periodiku
Jiřina, Marcel - Jiřina jr., M.
Separation in Data Mining Based on Fractal Nature of Data.
International Journal of Digital Information and Wireless Communications. Roč. 3, č. 1 (2013), s. 44-60. ISSN 2225-658X
Institucionální podpora: RVO:67985807
Klíčová slova: nearest neighbor * fractal set * multifractal * IINC method * correlation dimension
Kód oboru RIV: JC - Počítačový hardware a software
http://sdiwc.net/digital-library/separation-in-data-mining-based-on-fractal-nature-of-data.html

The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. We use a singularity exponent in classifiers that are based on distances of patterns to a given (classified) pattern. The approximation of so called probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in the form of a scaling exponent power of a distance is presented together with a way how to state it. Considering data as points in a metric space, three methods are based on transformed distances of neighbors of a given point in a multidimensional space via functions that use different estimates of scaling exponent. Classifiers – data separators utilizing knowledge about explored data distribution in a space and suggested expressions of the scaling exponent are presented. Experimental results on both synthetic and real-life data show interesting behavior (classification accuracy) of classifiers in comparison with other well-known approaches.
Trvalý link: http://hdl.handle.net/11104/0221997